Key Customer Insights from Analyzing Credit Card Data

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By Rosie Joyce

The financial sector has been continuously experiencing seismic disruptions over the past decade, and it’s mostly thanks to advancements in technology. As mentioned in a previous Payline post, there appears to be a greater shift towards more advanced technology, both in terms of security and convenience for customers. But beyond these factors, perhaps the most impactful change that is dominating the finance industry is analytics.

The world is generating an overwhelming amount of data every day. The World Economic Forum notes that the amount isn’t even quantifiable by kilobytes, megabytes, gigabytes, or even terabytes anymore. By this year, the entire digital universe is expected to reach 44 zettabytes of accumulated data.

It’s not surprising that the concept of Big Data exists. With the growing pool of data that each organization collects on a regular basis, there is now an entire field — Big Data — dedicated to parsing through all the raw information, analyzing it, and creating actionable insights that can lead to better business decisions. Beyond finance, Big Data is being embraced by varying industries, considering how organizations of all types are growing increasingly reliant on data to stay afloat. With the way the world produces a staggering amount of data, figuring out how to use that data has become critical for success. As such, Maryville University highlights how data science students can find employment across both small businesses and large corporations, government agencies, manufacturing firms, financial companies, tech firms, and non-profits.

In the financial field, a sector in which CIO notes that needs Big Data more than any other industry, the countless man-hours that are spent analyzing information can finally be eliminated to allow machines to take over and do it with more precision. Take the lending space, for example. The industry is built on the concept of loaning money with the assurance that the loan, coupled with interest, will eventually be paid back in full. If every person who applied for a loan were approved automatically, it would lead to catastrophic results. Every institution that offers money will no doubt be dried up within months. To prevent this from happening, they can turn to Big Data and use technology to create data points to determine someone’s eligibility, automate the audit process, make better credit evaluations, and so much more.

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Analyzing Credit Card Data for Fraud

Big Data can also be of big help when it comes to detecting financial crimes such as fraud and money laundering. In fact, Information Age highlights how high street banks like HSBC have been known to utilize data analytics to detect money laundering and combat fraud. The company is working with Google to build machine learning models which would help enhance their ability to identify genuine cases of financial crime and reduce false positives, allowing them to catch the bad guys.

Companies like NatWest are leveraging analytics solutions for fraud as well. In 2018, they announce a program called Corporate Fraud Insights, which combined AI and machine learning to flag any suspicious payment. The results were undoubtedly impressive — they were able to prevent over £7 million (nearly $9 million) in losses for their customers. However, while the advantages of using Big Data for purposes like this sound promising, there still needs to be an improvement for analytics programs to differentiate fraudulent transactions from false positives. This way, customer transactions will not be blocked as often due to overzealous security measures.

Analyzing Credit Card Data for Personal Finance

Meanwhile, when it comes to personal finance management, Big Data can contribute by offering customers a 360-degree view of their financial health and yield forward-looking advice to attain their financial goals. Digitalist Mag reiterates how AI assistants have become prevalent these days, serving as robo-advisers for customers to improve their financial literacy. To help consumers with the legwork of keeping tabs on their money, the assistant will help track their expenses and get to grips with their spending behavior. This allows the AI to predict the user’s actions and make smart recommendations as to how the consumer can improve further. What’s more, analytics can also help companies identify investment opportunities to offer to consumers based on their customer’s risk profiles and available funds, propose re-mortgaging house loans, or make use of previous spending data to gain a deeper understanding of trends and encourage them to improve their spending habits.

Financial institutions can get their hands on colossal amounts of data about their customers, and with the help of Big Data, they can successfully leverage the information and convert them into useful insights. With the data management financial services sector being more competitive than ever, those that want to stay ahead would benefit from taking a data-driven approach to emerge victorious.

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